The increasing demand to provide both reduced emissions and improvements in fuel economy for automobiles has led to the development of alternate forms of vehicular propulsion. Two prominent forms of such new automotive architecture that can be used as alternatives to conventional gasoline-based internal combustion engines (ICEs) are electric vehicles (EVs) and hybrid vehicles, also called hybrid-electric vehicles (HEVs). An EV uses a battery-driven electric traction motor to provide propulsion to the wheels, while HEVs employ a combination of ICE, fuel cells or related sources, as well as electric power. Both demonstrate improved fuel economy and reduced emissions compared to their ICE counterpart.
In either the EV or HEV variant, it is the presence of a rechargeable energy storage system (RESS) that provides the primary motive force. The cell chemistries in these RESSs may be in the form of lead-acid, nickel-cadmium, nickel-metal hydride and lithium chemistries. Of these, the lithium chemistries have shown particular promise as a lightweight, long-lasting and volumetrically efficient device for automotive applications. Lithium cell chemistries cannot be overcharged without damaging the active materials. As a result, control systems must be employed in order to prevent any single cell in a pack configuration from experiencing an overcharge condition as there is no natural mechanism for cell equalization (as in nickel-metal hydride chemistries). In addition, control systems must also be employed to prevent over discharge conditions which can also damage battery performance. As such, control or related monitoring systems are used to detect voltage, current, temperature and related parameters indicative of the amount of stored charge in the energy storage system that is available to do work relative to a fully charged state.
RESSs in general and batteries in particular, are commonly represented as equivalent circuit models. The values of the electrochemical mapping parameters in such models may be estimated in a variety of ways for the purpose of predicting the response of the RESS to various inputs and conditions. However, the circuit models and solution methods never provide a perfect representation of RESS behavior, but rather serve their purpose better under some conditions than in others. Other approaches may employ various calibration schemes to improve the accuracy of the models, but adding more resistance-capacitance elements may result in a slower, and more cumbersome system that is impractical for use in a vehicle. If the predicted electrochemical mapping parameters are inaccurate, the operation of the vehicle and the RESS may be negatively impacted. For example, if the pack level resistance of the RESS is predicted inaccurately, the pack level power limit calculation will also be incorrect. This may cause inefficient and imprecise control of the RESS and vehicle.
Accordingly, there remains a need for a method to accurately characterize RESSs, and estimate the electrochemical mapping parameters exhibited therein, without significant additions to the computational throughput.